Zero-shot Domain Generalization (ZSLDG):
Recently, CuMix [1]} introduced the problem of ZSLDG.
While [2]} defined variations in rotations of the same objects as different domains, such a restricted definition limits its real-world applicability. Differently, CuMix [1]} defines domains as different ways of depicting an object, as in sketch, painting, cartoon, etc., which is closer to practical use of such methods.
CuMix tackles the issue of domain shift through data augmentation by mixing and interpolating source domains, and handles semantic shifts by learning to project visual features to the semantic space.
This work also established a benchmark dataset, DomainNet, for this setting with an evaluation protocol, which we follow in this work for fair comparison.
However, relying on mixing source domains has a drawback – the resulting model could overfit to the source domains and their interpolations, thereby reducing generalizability to unseen domains [4]}.
Furthermore, directly mapping the visual space to the semantic space, as in [1]}, can lead to hubness issues (mapped points cluster as a hub due to low variance) [6]}, [7]}, thereby reducing class-discriminative capability. In contrast, our approach jointly handles the issues of domain and semantic shifts by learning a domain-agnostic latent space that is partitioned based on class-level (domain-invariant) semantic concepts, onto which the visual and semantic features are projected. Since domain invariance is enforced w.r.t. the visual-semantic joint distribution, it is less likely to overfit to seen domains (a common problem when domain-invariance is enforced w.r.t. marginal distribution of images [4]}, [9]}). In addition, our approach enables better interaction between visual and semantic spaces in a new latent space, thereby supporting model generalization to unseen classes in unseen domains.
